Slide 1
Sustainability Technical Accomplishments, Progress and Results
Presented by James Richardson, NAABB Sustainability Team Co-Leader
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Sustainability Task Framework
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Biomass Assessment Tool
Aspen Tech Modeling Software
Greenhouse gases, Regulated Emissions, and Energy Use in
Transportation
Farm-level Algal Risk Model
Applied Production Analysis
Computable General Equilibrium Global Simulation Model
PNNL
ANL
TAMU
NMSU
TAMU
Various
BAT
GREET
FARM
APA
CGE
ASPEN
Measures of Sustainability Generated by AISIM
•Risk Adjusted Profit•Prob. of Success•CAPEX/OPEX•GHG Emissions•Net Energy•Land Use•Marginal Cost•Water Use Needs
AISIM = NAABB Algae Integrated Simulation System
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NAABB AISIM
Experimental Results
1. 408,780 L/Hr harvesting2. Harvesting at 0.04 kWhr/m33. 950,000 L/Day Extraction though put
H&E
1. 2.5x Yield GMO2. Chlorella sorokiniana-14123. Temperature-PAR based estimates
AB
1. 5 g/l achieved in PBR2. 12 g/m2/day in Open Pond3. ARID Raceway design specifications
CULT BATGREETFARMAPA CGE
AISIM
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FARM = Farm-level Algal Risk Model • FARM was developed for NAABB economic sustainability analyses
• FARM is a Monte Carlo model that simulates an algae farm with an assumed debt structure and business plan using alternative technologies for biology, cultivation, harvesting, extraction, and alternative co-products
• FARM model harmonized with DOE algae-to-diesel model and found that FARM’s costs of production were within $0.05/gallon of the DOE Harmonized model
• Following the DOE harmonization report in FY12, FARM was validated against DOE model
• FARM’s costs of production were very close to DOE’s FY12 harmonized cost of production for diesel
• DOE $12.15/gal
• FARM $12.25/gal
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• BAT is a GPS based, national-scale resource and production assessment model for producing algal biofuels
• Used for DOE Harmonization report in FY12 • Analyzed 11,000 potential sites for growing algae in the United States
• Water temperature, evaporative losses, solar radiation, and rainfall estimated from 30 years of hourly meteorological data
• Simulated monthly biomass and lipid production and net water requirements for 30 years
• 30 years of monthly biomass and lipid production and net water use defined probability distributions which were used in FARM to simulate BAT’s nine best locations
BAT (Biomass Assessment Tool)
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000
Biomass PDF Nov-‐Feb
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000
Biomass PDF Mar, Apr, Sept,Oct
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000
Biomass PDF May-‐Aug
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BAT (Biomass Assessment Tool)
• FARM simulated the cost of production and probability of economic success for the median sites in the 9 best regions in DOE Harmonized FY12 report
• Three production scenarios analyzed for 9 sites: Generic Strain, Freshwater Chlorella, Saltwater Salina
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• Farms in all nine regions resulted in very low probabilities of economic success
• Non-zero probabilities of success in two of the three production scenarios occurred only when CAPEX and OPEX were reduced by 90%
• Most profitable scenarios were: Salina Saltwater, followed by Freshwater Chlorella
• Most profitable regions were: South Florida, Central Florida, Northern Florida and South Texas; with probabilities of success less than 50% even with 90% cuts in CAPEX and OPEX
• Costs per gallon for diesel were $20-$21/gallon for the four most profitable regions, after assuming 90% cuts in CAPEX and OPEX
FARM Results for Nine BAT Regions
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• GREET LCA analysis • Study of LCA seasonal variation • Simulated 30 years individually • Estimated costs of production and biomass production
GHGs and Energy Use for Algae with GREET
Year to year variation at some sites is large in some
months.
Variation at other sites smaller, but still significant.
Most sites similar in spring & summer, but many fail in
other months.
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GHGs and Energy Use for Algae with GREET
• BAT analyses using real species performed poorly for GHG emissions compared to theoretical FY12 Harmonization species (“generic”) analyzed with BAT
• Analysis of ARID with GREET • ARID with reduced circulation
energy • Improved low-productivity GHG
behavior • Higher productivity asymptote
requires improved energy efficiency for harvesting and processing with HTL-CHG
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Sustainability/Environmental Modeling Results Developed from Several Partners in NAABB
MTU-‐UOP LCA
Comparison
Monod kinetics 0.1 m/s 2, , 3, 4, 5 & 10 cm deep
NREL TEA
CFD
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Modeling and Analysis Efforts
MTU-‐UA-‐UOP Environmental impact of algae cul@va@on and environmental burden from nutrient, energy, and cul@va@on inputs
1. GHG 2. Energy Use 3. Land Use Change by Land Type 4. Nutrient Use and Burden
SIMAPRO vs GREET
U of PA
ASPEN
Conversion of algae extracts to biodiesel using the Albemarle-‐Ca@lin conversion process to transesterified biodiesel
1. GHG 2. Energy Use 3. CAPEX/OPEX 4. Profitability Analysis
UA
ASPEN and Chemcad
Sustainability of biodiesel from microalgae. Nutrient sustainability, environmental burden from cul@va@on, and impact of H&E. Pond liner impacts.
1. GHG 2. Energy Use 3. Nutrient Use 4. Eval of 6 different NAABB H&E Tech.
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Modeling and Analysis Efforts
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• Process models including: • Transesterification (UPenn) • Growth and harvesting (NREL and Pan Pacific) • Detailed thermodynamic-based end-to-end model (Pan Pacific)
• Sustainability and economic models were developed for a complex array of scenarios, technologies, and process steps
• Models include Life Cycle Assessment, Life Cycle Inventory, and Monte Carlo evaluation of profitability
• DOE will be receiving 500 pages of new LCA results and model analyses
• Examples: • (1) Handler, et al. 2012. "Evaluation of environmental impacts from microalgae cultivation in open-air
raceway ponds: Analysis of the prior literature and investigation of wide variance in predicted impacts." Algal Research. 1(2012) 83-92.
• (2) Silva, C. S., L. A. Fabiano, G. Cameron, and W. D. Seider, "Optimal Design of an Algae Oil Transesterification Process," in Karimi, I. A., and R. Srinivasan (Ed.), Proceedings of the 11th Int'l. Symp. on Proc. Sys. Eng., Singapore, 15-19 July 2012.
• (3) Dunlop, E., A. K. Coldrake, C. S. Silva, and W. D. Seider, "An Energy-limited Model of Algal Biofuel Production: Towards the Next Generation of Advanced Biofuels," AIChE J., submitted.
Modeling and Analysis Efforts
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• Barriers to improved modeling are • Consistent data collection procedures and standards • Collection of consistent variables across different procedures,
processes, labs and facilities • Scale too small to be meaningful for results on TEA/LCA • Little information on how lab scale experiments can be scaled up to
meaningful size • Inadequate information on economies of scale • Inadequate information on process conditions and documentation of
procedures, measurements, and variables
• Key to better models will be • Better Data • Experiments planned around the data and sufficient scale to be
meaningful
Modeling and Analysis Critical Factors
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Cultivation Data
• New data set contains more than 3,500 observations on cultivation parameters, sites in Pecos, TX, Las Cruces, NM, and Tucson, AZ
• Four years of data on temperature, PAR, precipitation, media mix,
water use and re-use, productivity, optical density, salinity, and lipid characterization provided for three sites
• Empirically estimated productivity and input factors
• Provide a field-scale estimate of productivity across seasons and locations using more than 50,000 liters of water
• Includes a variety of media use and recycle regimes as well as water chemistry and weather impacts on production
• Descriptive statistics and dataset will be made available to researchers
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• Preliminary estimates from the Pecos data show significant variation across seasons for the same strains • Estimated average AFDW g/m2/day for June was 12.56 +/- 4.58 (µ,σ2) • September production of biomass declines to a mean of 2.60 +/- 10.93 (µ,σ2)
• Shorter days and cooler temperatures in September and increasing differential between night time high and low are expected to explain the difference
• Cultivation data is being used to develop an Applied
Production Analysis (APA) to predict average annual production based on outdoor cultivation in raceway ponds
• APA biomass yield projections can be fed into FARM
• Using econometric methods to predict productivity data from the time series cultivation generated by NAABB projects
Cultivation Data Modeling
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Value of Co-products for NAABB
• Econometric analyses of value for lipid extracted algae (LEA) for animal and mariculture feed, and fertilizer
• Considered the chemical composition of LEA and whole algae, in particular: • Energy • Fat • Protein • Micronutrients • Amino acids
• Estimated the value of LEA based on historical values the feed ingredient market has placed on these nutritional attributes and the 2013 projected prices for feed ingredients for ruminants and mariculture
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• Assuming an LEA average chemical makeup of: • Nitrogen 3.25% • Phosphorus 0.49% • Potassium 0.65% • Carbon 31.4%
• Current prices for N, P, K, and Char the value of LEA is about $30/ton
Value of LEA as a Soil Amendment
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• Hedonic econometric models developed to estimate intrinsic value of LEA based on fractions of energy, protein, fat, etc. in LEA
• LEA intrinsic market value is $100 to $160 per ton less than soybean meal – $130-$190/ton in 2013 • Depending on specie, harvesting, and extraction • Valued higher the more oil residue remains • Must be of consistent quality and assay for livestock industry to adopt
Value of LEA for Animal Feed
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Value of LEA for Shrimp and Fish Feed • Based on fractions of energy, protein, fat, etc. in LEA and whole
algae; the value of these ingredient in mariculture rations are: • Whole algae averages $82/ton more than soybean meal – about $373/ton in
2013 • LEA averages $94/ton less than soybean meal – about $200/ton in 2013 • A non-market advantage of feeding LEA to mariculture is it replaces a portion
of fishmeal in the ration thus protecting the ocean’s fish population
0
200
400
600
800
1000
1200
1400
$/Ton
Time
Spirulina maxima NO floc Menhaden Fishmeal
0
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200
300
400
500
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700
$/Ton
Time
Spirulina maxima NO floc Soybean meal (high protein)
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• Developed a global computable general equilibrium (CGE) model of the world economy with biofuels sector and algae biofuels component
• Including trade in crude oil, land use change, and the effects on global food insecurity
• Model used to analyze the equilibrium effects of a 5bgy algal biofuels industry on energy, food, and agricultural markets
• Results show that by meeting 6.5 billion gallons of ethanol equivalent for the RFS2 mandate in 2030 with 5 bgy of algae biodiesel will reduce the number of food insecure people in the World relative to meeting this same level of production using grain base ethanol
• Also decreases U.S. oil dependency and reduces crude oil prices • Details in the NAABB final report
Global Economic Analysis for NAABB
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Summary of Sustainability Outcomes
6 Independent Life Cycle Analyses
GREET Analysis of NAABB Data
Detailed Microeconomic Analyses of Algae Fuels and Value of LEA
Detailed Macroeconomic Analysis of Algae Fuels
Updated Resource Assessment Tool
Comprehensive Cultivation, Characterization and Water Data Set
on Algae
•40% reduction in media cost
•1.7 x increase in demonstrated productivity (from 7 g/m2/day to 12 g/m2/day)
•Predicted decrease in cost of fuel from $12 gallon to $5.00-$7.00/gallon
•ARID shows promise to mitigate poor cool season performance
•HTL-CHG shows promise of increased fuel yield by LEA processing
•Predictions of algae biomass by location for more than 11,000 sites in the US
Optimized Process Scenarios
Key ResultsCompleted Models
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Sustainability Milestones and Deliverables
• Six ASPEN models developed for alternative situations and paradigms • Delivered to DOE and research community
• AISIM data integration and standardization • Experimental results from NAABB partners used in sustainability models –
ASPEN, LCA, and FARM • Cultivation data base delivered to research community and integrated in FARM • BAT model results integrated into LCA study and FARM
• AISIM modeling system fully integrated • FARM developed, harmonized, tested and allied to scenario analyses for farm
sustainability
Milestones (M), Decision Points (GN) and Deliverables (DL) Time (mo)
Status
F.1.DL.1: ASPEN process model for producing synthetic natural gas, liquid algal biofuel and chemical feedstock completed. (report)
12 Complete
F.DL.1: AISIMS data integration and standardization framework established. (report) 24 Complete
F.ML.1: Web based AISIMS modeling and database system fully implemented. (report) 36
Complete
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Questions Before the Scenario Analyses?
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Sustainability Scenario Analysis
HTL
CHG
HTL Bio-‐oil
HTL Effluent Water
Open Ponds
Algal Biology
ARID System
Electrocoagulation Harvesting
Centrifuge Harvesting Wet Solvent Extraction
HTL-CHG Extraction
Profit or Loss
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Economic Sustainability Analysis
• Experimental data from NAABB researchers used in FARM to estimate contributions to reducing costs of production and improving economic sustainability
1. 408,780 L/Hr harvesting2. Harvesting at 0.04 kWhr/m33. 950,000 L/Day Extraction though put
H&E
1. 2.5x Yield GMO2. Chlorella sorokiniana-14123. Temperature-PAR based estimates
AB
1. 12 g/m2/day in Open Pond2. ARID Raceway design specifications
CULT
FARM
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FARM Flowchart
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• Four technologies developed by NAABB are analyzed for a representative algae farm and compared to a base technology
• Where possible CAPEX and OPEX costs from DOE’s Harmonized report were scaled based on BAT’s annual biomass production levels for farm sites
• Pecos, TX and Tucson, AZ biomass, lipid, and water use probability distributions from BAT were used in the technology analyses and augmented for biomass production assumed for the advances reported by Algal Biology Team
Scenario Analysis Highlight NAABB Technologies
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Algae Farm Information for Scenario Analyses
Source: Extrapolated from DOE Harmoniza@on report 2012
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• Base scenario represents pre-NAABB technologies and production systems
• Four technologies selected to highlight the NAABB contributions to technology for reducing lipid costs, and increasing economic viability
• Scenarios highlighted technologies coming from NAABB Teams: • Algal Biology • Cultivation • Harvesting and Extraction
Technology Scenarios Analyzed
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Base Plus the Five Scenarios Analyzed
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Algae Farm Risks
• Farms sell algae crude oil and lipid extracted algae (LEA) or methane depending on extraction technology
• Risk associated with algae farming was incorporated into the FARM model by simulating random values from BAT’s probability distributions for biomass, lipid production and net water use
• Risk for price changes was incorporated by sampling from historical price probability distributions for input and output prices
• Each scenario simulated for 10 years and the planning horizon was repeated 500 iterations to incorporate full range of risk with best and worst cases appropriated weighted by risk of occurrence
• Repeated each scenario 100 times with systematic reductions in CAPEX and OPEX in 10% increments from zero to 90%
Slide 34 Slide 34
• Base Scenario • Open pond cultivation with paddlewheels for mixing • Low algae production rates • Centrifuges for harvesting • Wet solvent extraction with LEA byproduct
• Scenario 1 - Evaluates the improvements in harvesting technology • Electrocoagulation (EC) replaces centrifuges • Otherwise Scenario 1 is identical to Base Scenario
• Scenario 2 - Evaluates the improvements in extraction technology • Hydro Thermal Liquefaction-Catalytic Hydro Gasification (HTL-CHG) instead
of wet solvent extraction • Otherwise Scenario 2 is identical to Base Scenario
Scenarios Analyzed for Sustainability
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• Scenario 3 – Evaluates improvements in biology • Combines the improvements in Scenarios 1 & 2 from harvesting and
extraction, uses EC & HTL-CHG • Increased algae production rates (164% increase over baseline)
• Scenario 4 - Evaluates ARID cultivation technology • EC & HTL-CHG used for harvesting & extraction • Algae production rates (27% increase over baseline)
• Scenario 5 - ARID cultivation with improved biology and low cost harvesting and extraction options • EC & HTL-CHG used for harvesting & extraction • Increased algae production rates (216% increase over baseline)
Scenarios Analyzed for Sustainability
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Key Output Variables from FARM
• Probability of Success – Probability that the farm business will earn an average internal rate of return greater than the investor’s discount rate of 10%
• Marginal Cost (MC) of Production – Operating expense per gallon of lipid, ignoring all fixed costs
• Total Cost (TC) of Production – Marginal cost plus interest and dividend payments and depreciation per gallon
• Sensitivity Elasticity (ES) – shows how output variables change with a 1% change in an exogenous cost or production variable, i.e., percentage change in MC change when harvesting CAPEX is reduced 1%
Slide 37 Slide 37
• There are no combinations of reductions in CAPEX and OPEX that result in a non-zero probability of economic success.
• Even with 90% reductions in
CAPEX and OPEX the Total Cost ($/gallon) of lipid remains extremely high at $15.73/gallon.
• Similarly, even with 90% reductions in CAPEX and OPEX the Marginal Cost ($/gallon) of lipid remains extremely high at $13.11/gallon.
Sustainability Base
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 131.51 131.45 131.38 131.32 131.26 131.20 131.14 131.080.1 118.36 118.30 118.25 118.19 118.14 118.08 118.03 117.970.2 105.21 105.16 105.11 105.06 105.01 104.96 104.91 104.860.3 92.05 92.01 91.97 91.93 91.88 91.84 91.80 91.750.4 78.90 78.87 78.83 78.79 78.76 78.72 78.68 78.650.5 65.75 65.72 65.69 65.66 65.63 65.60 65.57 65.540.6 52.60 52.58 52.55 52.53 52.50 52.48 52.46 52.430.7 39.45 39.43 39.42 39.40 39.38 39.36 39.34 39.320.8 26.30 26.29 26.28 26.26 26.25 26.24 26.23 26.220.9 13.15 13.14 13.14 13.13 13.13 13.12 13.11 13.11
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 220.43 213.75 207.06 200.37 193.69 187.00 180.32 173.630.1 202.83 196.15 189.47 182.80 176.12 169.44 162.76 156.090.2 185.23 178.56 171.89 165.22 158.55 151.88 145.21 138.540.3 167.63 160.96 154.30 147.64 140.98 134.32 127.66 121.000.4 150.02 143.37 136.72 130.07 123.41 116.76 110.11 103.450.5 132.42 125.78 119.13 112.49 105.84 99.20 92.55 85.910.6 114.82 108.18 101.55 94.91 88.27 81.64 75.00 68.360.7 97.22 90.59 83.96 77.33 70.70 64.08 57.45 50.820.8 79.62 73.00 66.38 59.76 53.14 46.52 39.90 33.270.9 62.02 55.40 48.79 42.18 35.57 28.95 22.34 15.73
Fractional Reductions in the CAPEX
Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Fractional Reductions in the CAPEX
Slide 38 Slide 38
• There are no combinations of reductions in CAPEX and OPEX that result in non-zero probability of economic success.
• Even with 90% reductions in CAPEX and OPEX the Total Cost ($/gallon) of lipid remains extremely high $12.55/gallon. Total cost reduced compared to Scenario 1 of $15.73. EC is an improvement over centrifuge.
• Even with 90% reductions in CAPEX and OPEX the Marginal Cost ($/gallon) of lipid remains extremely high, but is improved over Scenario 1, $11.92/gallon.
Sustainability Scenario 1 - Harvesting Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 191.59 186.54 181.49 176.44 171.39 166.34 161.29 156.240.1 175.58 170.54 165.49 160.45 155.41 150.36 145.32 140.280.2 159.57 154.53 149.50 144.46 139.42 134.38 129.35 124.310.3 143.56 138.53 133.50 128.47 123.43 118.40 113.37 108.340.4 127.55 122.52 117.50 112.47 107.45 102.42 97.40 92.370.5 111.54 106.52 101.50 96.48 91.46 86.44 81.43 76.410.6 95.53 90.51 85.50 80.49 75.48 70.46 65.45 60.440.7 79.51 74.51 69.50 64.50 59.49 54.48 49.48 44.470.8 63.50 58.50 53.50 48.50 43.50 38.50 33.50 28.500.9 47.49 42.50 37.50 32.51 27.52 22.52 17.53 12.55
Fractional Reductions in the CAPEX
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 119.55 119.50 119.46 119.41 119.36 119.32 119.27 119.220.1 107.59 107.55 107.51 107.47 107.43 107.38 107.34 107.300.2 95.64 95.60 95.56 95.53 95.49 95.45 95.41 95.380.3 83.68 83.65 83.62 83.59 83.55 83.52 83.49 83.460.4 71.73 71.70 71.67 71.65 71.62 71.59 71.56 71.530.5 59.77 59.75 59.73 59.70 59.68 59.66 59.63 59.610.6 47.82 47.80 47.78 47.76 47.74 47.73 47.71 47.690.7 35.86 35.85 35.84 35.82 35.81 35.79 35.78 35.770.8 23.91 23.90 23.89 23.88 23.87 23.86 23.85 23.840.9 11.95 11.95 11.95 11.94 11.94 11.93 11.93 11.92
Fractional Reductions in the CAPEX
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Sustainability Scenario 2 - Extraction • HTL-CHG instead of wet
solvent extraction results in six acceptable probabilities of success, but only if significant reductions are made in CAPEX and OPEX.
• TC per gallon of lipid much lower compared to previous scenarios. In this comparison, HTL-CHG is much better than wet solvent extraction, with TC less than $4.00/gallon.
• If OPEX can be reduced by 50% and CAPEX can be reduced 70%, algal lipids could be competitive with fossil crude oil.
Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4%0.6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 93.4%0.7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%0.8 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 78.0% 100.0%0.9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0%
Fractional Reductions in the CAPEX
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 4.81 4.79 4.78 4.76 4.74 4.72 4.70 4.680.1 4.33 4.32 4.30 4.28 4.26 4.25 4.23 4.210.2 3.85 3.84 3.82 3.81 3.79 3.77 3.76 3.740.3 3.37 3.36 3.34 3.33 3.32 3.30 3.29 3.280.4 2.89 2.88 2.87 2.85 2.84 2.83 2.82 2.810.5 2.41 2.40 2.39 2.38 2.37 2.36 2.35 2.340.6 1.93 1.92 1.91 1.90 1.89 1.89 1.88 1.870.7 1.44 1.44 1.43 1.43 1.42 1.42 1.41 1.400.8 0.96 0.96 0.96 0.95 0.95 0.94 0.94 0.940.9 0.48 0.48 0.48 0.48 0.47 0.47 0.47 0.47
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 21.54 19.45 17.36 15.27 13.18 11.10 9.01 6.920.1 20.74 18.65 16.57 14.48 12.39 10.31 8.22 6.140.2 19.94 17.85 15.77 13.69 11.60 9.52 7.44 5.360.3 19.13 17.05 14.97 12.89 10.81 8.73 6.66 4.620.4 18.33 16.26 14.18 12.10 10.02 7.96 5.92 3.930.5 17.53 15.46 13.38 11.31 9.25 7.22 5.24 3.280.6 16.73 14.66 12.59 10.54 8.52 6.55 4.59 2.640.7 15.93 13.87 11.82 9.82 7.85 5.90 3.95 2.080.8 15.14 13.10 11.11 9.15 7.20 5.25 3.35 1.680.9 14.38 12.40 10.45 8.50 6.55 4.63 2.94 1.27
Fractional Reductions in the CAPEX
Slide 40 Slide 40
• Combining EC & HTL-CHG with increased biomass production with the same resources results in several acceptable probabilities of success, when combined with reductions in CAPEX and OPEX.
• The TC per gallon of lipid is much lower compared to previous scenarios, less than $4.00/gallon, for the same assumed reductions in CAPEX and OPEX.
• Algal lipids can be competitive with fossil fuel with reductions in costs and NAABB’s improvements in harvesting, extraction, and biology.
Sustainability Scenario 3 - Biology Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%0.6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.4% 63.6%0.7 0.0% 0.0% 0.0% 0.0% 0.6% 50.0% 99.8% 100.0%0.8 0.0% 0.0% 0.2% 37.6% 99.6% 100.0% 100.0% 100.0%0.9 0.0% 21.6% 99.6% 100.0% 100.0% 100.0% 100.0% 100.0%
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 10.33 9.65 8.96 8.28 7.59 6.91 6.22 5.540.1 9.58 8.89 8.21 7.52 6.84 6.16 5.47 4.790.2 8.82 8.14 7.45 6.77 6.09 5.41 4.74 4.060.3 8.06 7.38 6.71 6.03 5.36 4.69 4.03 3.370.4 7.32 6.65 5.99 5.33 4.67 4.01 3.36 2.720.5 6.62 5.96 5.31 4.67 4.02 3.37 2.73 2.090.6 5.96 5.32 4.68 4.03 3.39 2.75 2.11 1.470.7 5.33 4.69 4.05 3.41 2.77 2.13 1.50 0.910.8 4.71 4.07 3.43 2.79 2.17 1.61 1.13 0.690.9 4.09 3.46 2.87 2.36 1.88 1.42 0.97 0.57
Fractional Reductions in the CAPEX
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 1.89 1.89 1.88 1.87 1.87 1.86 1.86 1.850.1 1.70 1.70 1.69 1.69 1.68 1.68 1.67 1.660.2 1.51 1.51 1.50 1.50 1.49 1.49 1.48 1.480.3 1.33 1.32 1.32 1.31 1.31 1.30 1.30 1.290.4 1.14 1.13 1.13 1.12 1.12 1.12 1.11 1.110.5 0.95 0.94 0.94 0.94 0.93 0.93 0.93 0.920.6 0.76 0.75 0.75 0.75 0.75 0.74 0.74 0.740.7 0.57 0.57 0.56 0.56 0.56 0.56 0.56 0.550.8 0.38 0.38 0.38 0.37 0.37 0.37 0.37 0.370.9 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.18
Fractional Reductions in the CAPEX
Slide 41 Slide 41
• ARID cultivation system, along with EC & HTL-CHG returns several non-zero probabilities of economic success, indicating that it could be a viable cultivation system.
• With severe reductions in CAPEX and OPEX, e.g., 70% CAPEX and 70% OPEX reduction, algal fuels can become competitive with fossil fuel at $3.51/gallon.
• Similarly, with discounts in OPEX, e.g., 20% or greater OPEX reductions, algal fuels can become competitive with current fuel sources, less than $2.00/gallon.
Sustainability Scenario 4 - Cultivation Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 100.0%0.1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.2% 100.0%0.2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 31.6% 100.0%0.3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 89.8% 100.0%0.4 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 99.8% 100.0%0.5 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% 100.0% 100.0%0.6 0.0% 0.0% 0.0% 0.0% 0.0% 11.4% 100.0% 100.0%0.7 0.0% 0.0% 0.0% 0.0% 0.0% 64.6% 100.0% 100.0%0.8 0.0% 0.0% 0.0% 0.0% 0.0% 97.8% 100.0% 100.0%0.9 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 100.0%
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 12.68 11.17 9.67 8.18 6.72 5.29 3.89 2.570.1 12.33 10.83 9.34 7.86 6.43 5.02 3.62 2.370.2 11.99 10.49 9.01 7.56 6.15 4.74 3.36 2.170.3 11.65 10.16 8.70 7.27 5.87 4.47 3.15 1.970.4 11.31 9.84 8.40 6.99 5.59 4.20 2.94 1.770.5 10.98 9.53 8.12 6.72 5.32 3.94 2.74 1.580.6 10.67 9.24 7.84 6.44 5.04 3.72 2.54 1.400.7 10.36 8.95 7.56 6.16 4.77 3.51 2.35 1.290.8 10.07 8.67 7.28 5.89 4.51 3.31 2.17 1.190.9 9.79 8.40 7.01 5.62 4.28 3.11 2.00 1.04
Fractional Reductions in the CAPEX
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 2.43 2.42 2.40 2.39 2.37 2.36 2.35 2.330.1 2.19 2.17 2.16 2.15 2.14 2.12 2.11 2.100.2 1.94 1.93 1.92 1.91 1.90 1.89 1.88 1.870.3 1.70 1.69 1.68 1.67 1.66 1.65 1.64 1.630.4 1.46 1.45 1.44 1.43 1.42 1.42 1.41 1.400.5 1.21 1.21 1.20 1.19 1.19 1.18 1.17 1.170.6 0.97 0.97 0.96 0.96 0.95 0.94 0.94 0.930.7 0.73 0.72 0.72 0.72 0.71 0.71 0.70 0.700.8 0.49 0.48 0.48 0.48 0.47 0.47 0.47 0.470.9 0.24 0.24 0.24 0.24 0.24 0.24 0.23 0.23
Fractional Reductions in the CAPEX
Slide 42 Slide 42
Sustainability Scenario 5 – Cultivation and Biology
• ARID cultivation system, with biomass production increases, EC & HTL-CHG returns the most non-zero probabilities of economic success, but cuts in CAPEX and OPEX will be necessary.
• With reductions in CAPEX and OPEX algal fuels can be competitive with fossil fuels. A 40% reduction in CAPEX and 30% reduction in OPEX has TC of $3.14/gallon.
• With the given improvements in biological, harvesting, and extraction technologies algal production can become a viable source of crude oil.
Probability of Economic SuccessOpen PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 0.0% 0.0% 0.0% 7.2% 97.0% 100.0% 100.0% 100.0%0.1 0.0% 0.0% 0.0% 41.4% 100.0% 100.0% 100.0% 100.0%0.2 0.0% 0.0% 1.8% 87.6% 100.0% 100.0% 100.0% 100.0%0.3 0.0% 0.0% 18.2% 100.0% 100.0% 100.0% 100.0% 100.0%0.4 0.0% 0.2% 64.2% 100.0% 100.0% 100.0% 100.0% 100.0%0.5 0.0% 5.0% 97.0% 100.0% 100.0% 100.0% 100.0% 100.0%0.6 0.0% 36.6% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%0.7 1.0% 85.4% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%0.8 14.2% 99.8% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%0.9 57.4% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Fractional Reductions in the CAPEX
Average Total Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 4.85 4.26 3.67 3.08 2.50 1.92 1.40 0.930.1 4.67 4.08 3.49 2.90 2.33 1.79 1.31 0.850.2 4.49 3.90 3.31 2.73 2.19 1.69 1.23 0.820.3 4.31 3.72 3.14 2.58 2.08 1.61 1.16 0.830.4 4.12 3.54 2.98 2.47 1.99 1.53 1.14 0.830.5 3.94 3.38 2.86 2.37 1.90 1.47 1.12 0.770.6 3.78 3.25 2.76 2.28 1.82 1.43 1.07 0.690.7 3.64 3.14 2.66 2.19 1.76 1.38 0.98 0.620.8 3.53 3.05 2.57 2.11 1.69 1.29 0.89 0.550.9 3.43 2.95 2.47 2.02 1.60 1.19 0.80 0.45
Fractional Reductions in the CAPEX
Average Marginal Cost per Gallon for Lipid ($/Gallon)Open PondFraction OPEX 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0 1.22 1.21 1.20 1.20 1.19 1.19 1.18 1.180.1 1.09 1.09 1.08 1.08 1.07 1.07 1.06 1.060.2 0.97 0.97 0.96 0.96 0.95 0.95 0.94 0.940.3 0.85 0.85 0.84 0.84 0.83 0.83 0.83 0.820.4 0.73 0.73 0.72 0.72 0.72 0.71 0.71 0.710.5 0.61 0.60 0.60 0.60 0.60 0.59 0.59 0.590.6 0.49 0.48 0.48 0.48 0.48 0.47 0.47 0.470.7 0.36 0.36 0.36 0.36 0.36 0.36 0.35 0.350.8 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.240.9 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
Fractional Reductions in the CAPEX
Slide 43 Slide 43
Sustainability Results for a 4,850 ha Algae Farm
Range of costs reported above are for zero reduc3ons in CAPEX and OPEX vs. the smallest reduc3on in CAPEX and OPEX to achieve economic viability
BaseScenario 1 Harvesting
Scenario 2 Extraction
Scenario 3Harv/ExtrBiology
Scenario 4 Harv/ExtrCultivation
Scenario 5 Harv/ExtrCultivationBiology
Reductionin CAPEX
90% 90% 80% 70% 70% 40%
Reductionin OPEX
90% 90% 80% 70% 70% 50%
P(Success) 0.0 – 0.0 0.0 – 0.0 0.0 –78.0%
0.0 – 50% 0.0 –64.6%
0.0 –97.0%
Total Cost$/gal
233.80-15.73
201.69 –12.55
21.54 –3.35
10.33 –2.15
12.68 –3.51
4.85 –2.86
Slide 44 Slide 44
• Cultivation, harvesting, and extraction CAPEX are all major cost sources
• Costs have to be dramatically cut in all areas to insure profitability
CAPEX OPEX
CAPEX and OPEX for Scenario 3
Cultivation
Harvesting
Extraction
Cultivation
Harvesting
Extraction
43%
20%
37% 53%
19% 28%
Slide 45 Slide 45
Guide to Reducing Costs
• Cost reductions will be essential for a profitable algal industry
• FARM includes a tool for determining where cost reductions will be most beneficial
• Sensitivity elasticities in the model show the percentage reduction in total cost of production for a one percentage reduction in a particular input cost
• Also used to show percentage increases in income for a one percent increase in biomass production or a one percent decrease in costs
Slide 46 Slide 46
For Scenario 3: a 1% decrease in harvesting CAPEX reduces TC 0.2% a 1% decrease in extraction catalyst cost TC 0.45% a 1% increase in biomass production reduced TC 1.18%
Sensitivity Elasticity – Total Costs Scenario 3
Biomass Production Multiplier
CHG Catalyst Cost
Cultivation CAPEX
Harvesting CAPEX
Extraction CAPEX
EC Plate Replacement Cost
Maintenance
Nutrients
Labor & Overhead
Non-‐Harvesting Electricity
Insurance
Harvesting Electricity
Utilities
-‐1.4 -‐1.2 -‐1 -‐0.8 -‐0.6 -‐0.4 -‐0.2 0 0.2 0.4 0.6
Frac3onal Changes in Total Costs for a 1% change
Slide 47 Slide 47
For Scenario 3: a 1% decrease in harvesting CAPEX increases NCI 0.25% a 1% decrease in extraction catalyst cost increases NCI 1.45% a 1% increase in biomass production increases NCI 1.65%
Sensitivity Elasticity – Net Cash Income Scenario 3
Biomass Production Multiplier
CHG Catalyst Cost
Cultivation CAPEX
Harvesting CAPEX
Extraction CAPEX
EC Plate Replacement Cost
Maintenance
Nutrients
Labor & Overhead
Non-‐Harvesting Electricity
Insurance
Harvesting Electricity
Utilities
-‐2 -‐1.5 -‐1 -‐0.5 0 0.5 1 1.5 2
Frac3onal Changes in Total Costs for a 1% change
Slide 48 Slide 48
• Increasing biomass productivity and crop protection 10% without changing CAPEX and OPEX • Increases net cash income 16%
• 10% Reduction in harvesting CAPEX and OPEX ($86.1 million) • Increases net cash income 4.5% • A 10% reduction in harvesting CAPEX is $ 46.9 million • A 10% reduction in harvesting OPEX is $ 39.2 million
• 10% Reduction in extraction CAPEX and OPEX ($60.9million) • Increases net cash income 16.5% • A 10% reduction in extraction CAPEX is $25.4 million • A 10% reduction in extraction OPEX is $35.5 million
• 10% Reduction in cultivation CAPEX and OPEX ($78.7 million) • Increases net cash income 3% • A 10% reduction in cultivation CAPEX is $54.6 million • A 10% reduction in cultivation OPEX is $24.1 million
Critical Success Factors for Sustainability
Slide 49 Slide 49
Cut CAPEX and OPEX
Slide 50 Slide 50
• Use data for cultivation, characterization, and processes generated during the final months of NAABB to create empirically based estimates of: • Production potential and viability based on outdoor cultivation data
• Impact of cultivation variables on production of biomass and lipids (quality and quantity)
• Use APA projected biomass production in FARM to generate estimates of profitability based on field-scale data
• Scenario analyses using FARM extended to additional technologies
developed by NAABB that were not presented in this report
• Water quality impacts on production
• Produced water algae production
• Alternative media formulas and costs
• Alternative harvesting technologies
• Additional biology applications using newer GMO strains
Future Work
Slide 51 Slide 51
Questions on Scenario Analysis?